Data for the competition is also available for immediate exploration through
SocialSplunk. A login code will be sent to you when you register for the competition. Haven't received your login code yet? Email support@kaggle.com and we'll get you sorted out.

The evaluation data currently only drive the public leaderboard. The private leaderboard is not yet active. To avoid a situation where people could look up the answers on the web, we have a two-phase data release.

First Data Release

The data for the first release are drawn from a 6-week period of blog posts and "likes" of those blog posts. The training data consist of the first 5 weeks of posts and "likes" that occurred during those 5 weeks. This data files provided (some of which contain
redundant information in different forms):

trainUsers.json: One JSON dictionary per line, where each line corresponds to one WordPress.com user, and the fields are:

"uid": ID for user

"inTestSet" : is this user in the test set (one of the users you're required to make predictions about)

"likes" : a list of dictionaries, one for each training like by this user, only containing like by this user during the training period:

"blog": blog liked

"post_id": post liked (randomly assigned unique identifier)

"like_dt": date of like

trainPostsThin.json: One JSON dictionary per line, where each line corresponds to one blog post from the training set (first 5 weeks). The fields are::

"blog": blog ID

"post_id": post IDs

"likes": list of dictionaries, one for each like for this post, only containing likes from the training period (first 5 weeks). Later likes from the same post are not included. The fields are:

date

uid (user id)

trainPosts.json: This is like trainPostsThin but with many more fields about the post, including its text, tags, and categories.

testPosts.json: This is like trainPosts but without the "likes".

testPostsThin.json: This is like trainPostsThin but without the "likes". (So it's very thin!)

test.csv: List of users in the test data set. These are the users about whom you should make predictions.

kaggle-stats-blogs-20111123-20120423.json: 6 months of aggregate statistics about each blog.

blog_id

num_posts

num_likes (note that a blog may have zero posts and more than zero likes in the 6 month period, since likes on posts prior to the 6 months are included)

kaggle-stats-users-20111123-20120423.json: 6 months of aggregate statistics about each user's like behavior.

user_id

num_likes (in previous 6 months)

like_blog_dist -- which blogs this user liked and how often

The "test" users are restricted to users who have "liked" at least 1 post in the test period and at least 5 posts in the train period. While the test set is restricted like this, no such restriction has been made on the data provided in
the training set.

At the end of September 1, 2012 (measured by UDT), the contest will be closed to new submissions and the only submissions eligible to win will be those that have attached the complete code necessary for generating the submission. At that point, the second
phase of data will be released.

Second Data Release

The private leaderboard (final evaluation) data will be drawn from a future 6 week period. These 6 future weeks will be divided in the same way as the First Data Release, and prior aggregate data from the beginning of the new 6-week period will also be available.

After the Second Data Release, contestants will have one week to generate predictions based on their previously submitted code. This previously submitted code must be able to generate the new predictions with no human input or judgment. At the end of that
week, preliminary winners will be announced. Kaggle will then make public the code that the preliminary winners had previously submitted with their entries. There will then be a two week period during which participants or other individuals will have a chance
to replicate the results and (potentially) challenge the preliminary winners with violating the contest rules, or not having presented code that creates the results claimed.

In case of disputes during the verification process, Kaggle will select a panel to adjudicate.